"""simple docstring"""


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
	import transformers





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Any:




						'''simple docstring'''



						random.seed(__lowerCAmelCase							)
						np.random.seed(__lowerCAmelCase							)
						torch.manual_seed(__lowerCAmelCase							)
						torch.cuda.manual_seed_all(__lowerCAmelCase							)
						# ^^ safe to call this function even if cuda is not available






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase = 0.9_9_9_9      ,					_lowerCamelCase = 0.0      ,					_lowerCamelCase = 0      ,					_lowerCamelCase = False      ,					_lowerCamelCase = 1.0      ,					_lowerCamelCase = 2 / 3      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					**_lowerCamelCase      ,					):

										if isinstance(_lowerCamelCase      ,					torch.nn.Module ):
																lowerCamelCase__   =(
																    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
																    "Please pass the parameters of the module instead."
																)
																deprecate(
																    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"      ,					"1.0.0"      ,					_lowerCamelCase      ,					standard_warn=_lowerCamelCase      ,					)
																lowerCamelCase__   =parameters.parameters()

																# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																lowerCamelCase__   =True

										if kwargs.get("max_value"      ,					_lowerCamelCase ) is not None:
																lowerCamelCase__   ="The `max_value` argument is deprecated. Please use `decay` instead."
																deprecate("max_value"      ,					"1.0.0"      ,					_lowerCamelCase      ,					standard_warn=_lowerCamelCase )
																lowerCamelCase__   =kwargs["max_value"]

										if kwargs.get("min_value"      ,					_lowerCamelCase ) is not None:
																lowerCamelCase__   ="The `min_value` argument is deprecated. Please use `min_decay` instead."
																deprecate("min_value"      ,					"1.0.0"      ,					_lowerCamelCase      ,					standard_warn=_lowerCamelCase )
																lowerCamelCase__   =kwargs["min_value"]

										lowerCamelCase__   =list(_lowerCamelCase )
										lowerCamelCase__   =[p.clone().detach() for p in parameters]

										if kwargs.get("device"      ,					_lowerCamelCase ) is not None:
																lowerCamelCase__   ="The `device` argument is deprecated. Please use `to` instead."
																deprecate("device"      ,					"1.0.0"      ,					_lowerCamelCase      ,					standard_warn=_lowerCamelCase )
																self.to(device=kwargs["device"] )

										lowerCamelCase__   =None

										lowerCamelCase__   =decay
										lowerCamelCase__   =min_decay
										lowerCamelCase__   =update_after_step
										lowerCamelCase__   =use_ema_warmup
										lowerCamelCase__   =inv_gamma
										lowerCamelCase__   =power
										lowerCamelCase__   =0
										lowerCamelCase__   =None  # set in `step()`

										lowerCamelCase__   =model_cls
										lowerCamelCase__   =model_config

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   =model_cls.load_config(_lowerCamelCase      ,					return_unused_kwargs=_lowerCamelCase )
										lowerCamelCase__   =model_cls.from_pretrained(_lowerCamelCase )

										lowerCamelCase__   =cls(model.parameters()      ,					model_cls=_lowerCamelCase      ,					model_config=model.config )

										ema_model.load_state_dict(_lowerCamelCase )
										return ema_model

				def 						_a      ( self      ,					_lowerCamelCase ):
										if self.model_cls is None:
																raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__." )

										if self.model_config is None:
																raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__." )

										lowerCamelCase__   =self.model_cls.from_config(self.model_config )
										lowerCamelCase__   =self.state_dict()
										state_dict.pop("shadow_params"      ,					_lowerCamelCase )

										model.register_to_config(**_lowerCamelCase )
										self.copy_to(model.parameters() )
										model.save_pretrained(_lowerCamelCase )

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =max(0      ,					optimization_step - self.update_after_step - 1 )

										if step <= 0:
																return 0.0

										if self.use_ema_warmup:
																lowerCamelCase__   =1 - (1 + step / self.inv_gamma) ** -self.power
										else:
																lowerCamelCase__   =(1 + step) / (10 + step)

										lowerCamelCase__   =min(_lowerCamelCase      ,					self.decay )
										# make sure decay is not smaller than min_decay
										lowerCamelCase__   =max(_lowerCamelCase      ,					self.min_decay )
										return cur_decay_value

				@torch.no_grad()
				def 						_a      ( self      ,					_lowerCamelCase ):
										if isinstance(_lowerCamelCase      ,					torch.nn.Module ):
																lowerCamelCase__   =(
																    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
																    "Please pass the parameters of the module instead."
																)
																deprecate(
																    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"      ,					"1.0.0"      ,					_lowerCamelCase      ,					standard_warn=_lowerCamelCase      ,					)
																lowerCamelCase__   =parameters.parameters()

										lowerCamelCase__   =list(_lowerCamelCase )

										self.optimization_step += 1

										# Compute the decay factor for the exponential moving average.
										lowerCamelCase__   =self.get_decay(self.optimization_step )
										lowerCamelCase__   =decay
										lowerCamelCase__   =1 - decay

										lowerCamelCase__   =contextlib.nullcontext
										if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																import deepspeed

										for s_param, param in zip(self.shadow_params      ,					_lowerCamelCase ):
																if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																						lowerCamelCase__   =deepspeed.zero.GatheredParameters(_lowerCamelCase      ,					modifier_rank=_lowerCamelCase )

																with context_manager():
																						if param.requires_grad:
																												s_param.sub_(one_minus_decay * (s_param - param) )
																						else:
																												s_param.copy_(_lowerCamelCase )

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =list(_lowerCamelCase )
										for s_param, param in zip(self.shadow_params      ,					_lowerCamelCase ):
																param.data.copy_(s_param.to(param.device ).data )

				def 						_a      ( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None ):
										lowerCamelCase__   =[
										    p.to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase ) if p.is_floating_point() else p.to(device=_lowerCamelCase )
										    for p in self.shadow_params
										]

				def 						_a      ( self ):
										return {
										    "decay": self.decay,
										    "min_decay": self.min_decay,
										    "optimization_step": self.optimization_step,
										    "update_after_step": self.update_after_step,
										    "use_ema_warmup": self.use_ema_warmup,
										    "inv_gamma": self.inv_gamma,
										    "power": self.power,
										    "shadow_params": self.shadow_params,
										}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =[param.detach().cpu().clone() for param in parameters]

				def 						_a      ( self      ,					_lowerCamelCase ):
										if self.temp_stored_params is None:
																raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`" )
										for c_param, param in zip(self.temp_stored_params      ,					_lowerCamelCase ):
																param.data.copy_(c_param.data )

										# Better memory-wise.
										lowerCamelCase__   =None






				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =copy.deepcopy(_lowerCamelCase )

										lowerCamelCase__   =state_dict.get("decay"      ,					self.decay )
										if self.decay < 0.0 or self.decay > 1.0:
																raise ValueError("Decay must be between 0 and 1" )

										lowerCamelCase__   =state_dict.get("min_decay"      ,					self.min_decay )
										if not isinstance(self.min_decay      ,					_lowerCamelCase ):
																raise ValueError("Invalid min_decay" )

										lowerCamelCase__   =state_dict.get("optimization_step"      ,					self.optimization_step )
										if not isinstance(self.optimization_step      ,					_lowerCamelCase ):
																raise ValueError("Invalid optimization_step" )

										lowerCamelCase__   =state_dict.get("update_after_step"      ,					self.update_after_step )
										if not isinstance(self.update_after_step      ,					_lowerCamelCase ):
																raise ValueError("Invalid update_after_step" )

										lowerCamelCase__   =state_dict.get("use_ema_warmup"      ,					self.use_ema_warmup )
										if not isinstance(self.use_ema_warmup      ,					_lowerCamelCase ):
																raise ValueError("Invalid use_ema_warmup" )

										lowerCamelCase__   =state_dict.get("inv_gamma"      ,					self.inv_gamma )
										if not isinstance(self.inv_gamma      ,					(float, int) ):
																raise ValueError("Invalid inv_gamma" )

										lowerCamelCase__   =state_dict.get("power"      ,					self.power )
										if not isinstance(self.power      ,					(float, int) ):
																raise ValueError("Invalid power" )

										lowerCamelCase__   =state_dict.get("shadow_params"      ,					_lowerCamelCase )
										if shadow_params is not None:
																lowerCamelCase__   =shadow_params
																if not isinstance(self.shadow_params      ,					_lowerCamelCase ):
																						raise ValueError("shadow_params must be a list" )
																if not all(isinstance(_lowerCamelCase      ,					torch.Tensor ) for p in self.shadow_params ):
																						raise ValueError("shadow_params must all be Tensors" )



"""simple docstring"""


import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       **__lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =AutoConfig.from_pretrained(__lowerCAmelCase  ,       **__lowerCAmelCase							)
						lowerCamelCase__   =AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase							)
						model.save_pretrained(__lowerCAmelCase							)
						AutoTokenizer.from_pretrained(__lowerCAmelCase							).save_pretrained(__lowerCAmelCase							)
						return model


if __name__ == "__main__":
	fire.Fire(save_randomly_initialized_version)


